Identifying Financial Market Trend Reversal Behavior With Structures of Price Activities Based on Deep Learning Methods

Abstract

In recent years, with the popularity of artificial intelligence (AI) applications, financial market forecasting based on deep learning models has gotten more attention in academia and industry. According to statistics, a long short-term memory network (LSTM) is the first choice for deep learning to deal with the financial time series predicting problems due to its internal memory that can process incoming input with the previous state. However, most of the research used the raw financial time series data composed of opening, closing, highest, lowest price, and transactional time as learning features to feed into models. This study proposed a novel approach focusing on appropriate structures that can organize the price activities as a bell shape curve and identify the greed and fear in the market with deep convolutional neural networks. In addition, this study also considered the influence of time causality on patterns. It is difficult to accurately catch the rapid changes in the market, especially the causal link between various patterns and trend reversal behavior while using only static features extracted by AI models. We designed disparate methods while generating images that could keep the time-variant features on structures and be extracted by convolutional neural networks. Each image was labeled as the upward trend to downward trend, downward trend to upward trend, and trend did not change according to reversals of overall price direction. Finally, to evaluate the availability of the trained model, two-stage experiments were carried out. The first stage of the experiment mainly evaluated the accuracy and profitability of trades following the models, and the second stage considered practical trading rules such as the stop-loss mechanism. The results of the first and second stages showed the proposed models had better profitability and extremely well-matched classification capabilities when compared with the state-of-theart deep learning models.